Artificial Intelligence
Overview
Artificial intelligence is a study on how to comprehend the intelligent human behaviours on a computer. The final goal of it is to make a computer that can learn, plan, and solve problems unconventionally.A few topics in AI are problem solving, reasoning, planning, natural language, understanding, computer vision, automatic programming, machine learning etc. Though, these topics are closely related with each other. For example, the information acquired through learning can be used both for reasoning and also for problem solving. The approaches for problem solving are useful for planning and reasoning.
Duration
2 Days
Pre-Requisites
- Participants must have strong grip on Mathematics
- Participants must have strong knowledge of programming languages
- Participants must be able to write algorithm for finding patterns and learning
- Participants must have strong data analytics skills
- Participants must have good knowledge of Discrete mathematics
- Participants must have strong determination to learn machine learning languages
Course Outline
- Understand the difference between Machine learning, Deep learning, Data Science, Artificial Intelligence
- Artificial Intelligence in real world-applications
- Use Cases in telecom field
- Artificial Intelligence project life cycle
- Learning paths for various skill sets
- Practical aspects of implementation.
- Components of AI architecture
- Cloud based platforms
- Proprietary tools
- Open-source tools, Platforms
- Feature pre-processing
- Exploratory Data Analysis
- Data Validation rules
- Data cleaning techniques
- Data Preparation for analysis
- Distance metrics
- Algorithms used in AI
- Model accuracy check various techniques and Overview
- MSE
- Confusion Matrix
- Accuracy
- How to validate a model?
- What is a best model?
- Types of data
- Types of errors
- Improve accuracy of model
- AI Best Practices
- Debugging Strategies